View article

Coreference analysis, also known as record linkage or identity uncer-tainty, is a difficult and important problem in natural language process-ing, databases, citation matching and many other tasks. This paper intro-duces several discriminative, conditional-probability models for coref-erence analysis, all examples of undirected graphical models. Unlike many historical approaches to coreference, the models presented here are relational--they do not assume that pairwise coreference decisions should be made independently from each other. Unlike other relational models of coreference that are generative, the conditional model here can incorporate a great variety of features of the input without having to be concerned about their dependencies--paralleling the advantages of con-ditional random fields over hidden Markov models. We present positive results on noun phrase coreference in two standard text data sets.